In this paper, a novel method inspired by image2image translation [1]} is introduced and tested on vibration signals to generate fault data from normal data. Pairs of normal and fault data are fed as inputs to the network at a given condition. After the training phase, the network can generate new fault data under different conditions. We assume no fault sample is available in other conditions, and this data generation is only done using normal data. The efficiency of the proposed method and the quality of generated data are evaluated using different classifiers and visualization methods. The paper's organization is as follows: A literature review is done in Section 2. Next, Section 3 introduces some background theories and Section 4 elaborates on the proposed method. The method is tested on the CWRU dataset and verified in Section 5, and finally, conclusions are available in Section 6.
